• Neural networks are a subset of machine learning, which is a technique used to help computers learn using training that is modeled on results gleaned from large data sets.  As such, neural networks are an attempt mimic human thinking, specifically how biological neurons are thought to signal to one another. The Google search engine, with its many interrelated nodes, is a good best example of a neural network. It is probably the largest in existence as it has the task of providing the instant and accurate results that users demand.
• Deep learning could be defined as a form of machine learning that is firmly based on AI neural networks. In some ways, deep learning can be viewed as advanced neural networks as it takes the basic capabilities of neural networks to a whole new level.

Also see: The Pros and Cons of Deep Learning

Neural Networks vs. Deep Learning

Neural networks and deep learning are often confused; the terms are sometimes used interchangeably in general AI discussion.

A good way to differentiate them: deep learning goes deeper than standard neural networks – hence its name. How? By implementing more layers within a neural network. This depth of analysis, then, involves more time, training, and investment.

Neural networks requires less time than deep learning

Neural networks, while powerful in synthesizing AI algorithms, typically require less resources. In contrast, as deep learning platforms take time to get trained on complex data sets to be able to analyze them and provide rapid results, they typically take far longer to develop, set up and get to the point where they yield accurate results.

Deep learning goes deeper

Many applications don’t need deep learning, with its ability to plumb the depths of AI’s capacity. Neural networks also offer powerhouse performance, though less so; yet  increasingly, even basic AI tools are harnessing relatively simple neural networks as part of their operations. But as complexity rises, deep learning must be introduced to provide the expected level of performance and accuracy.

Neural networks need less investment

Basic neural networks require less financial outlay than deep learning, which needs far more processing power (such as Graphics Processing Units, often supplied by Nvidia), more expensive hardware and more advanced software.

Neural networks represent a major advance in AI technology. They are increasingly used in almost all the latest AI applications. Deep learning steps it up further but has a more limited set of applications. It typically requires more time and resources to set up and analyze but provides deeper and better conclusions, whereas neural networks solutions can often be arrived at faster as they are more narrowly defined and apply to a smaller data set.

Now let’s take a deeper look at neural networks and deep learning: 

Also see: Best Artificial Intelligence Software 2023

What is a Neural Network?

Neural networks are software constructs that are comprised of various layers such as input layers, hidden layers and output layers. Each node functions like an artificial neuron. It connects to other nodes and sends data to other layers of the network. They must be trained, must have their accuracy tuned, but offer the ability to classify data rapidly.

Training

Neural networks are trained on data as a way of learning and improving their conclusions over time. As with all AI deployments, the more data it’s trained on the better. Neural networks must be fine-tuned for accuracy over and over as part of the learning process to transform them into powerful artificial intelligence tools. Fortunately for many businesses, plenty of neural networks have been trained for years – far before the current craze inspired by ChatGPT – and are now powerful business tools.

Data classification

Once trained and tuned, neural networks can classify data and cluster it at incredible velocities. As a result, neural networks can take on complex AI tasks such as in speech and image recognition and complete them in minutes. Significantly, this rapid speed is still increasing, suggesting this already brief time period will fall to seconds.

Also see: Top Generative AI Apps and Tools

Neural Network Use Cases

Neural networks are now involved in more and more types of AI. This includes speech and image recognition, advanced search, and generative AI. In particular, the generative AI use cases are being adopted at a rapid pace by businesses.

Speech recognition

There are so many accents, languages, idioms and dialects that the area of speech recognition has been a problematic area of technology for many years. AI backed by neural networks provides the processing and differentiating capabilities needed to minimize these challenges. What’s required is training, across time and many dialects; you’ll notice that more voice-based chatbots are now able to recognize more idioms and dialects.

Image recognition

Similarly, image recognition poses real difficulties due to the myriad of objects potentially on display. Neural networks help to increase accuracy and speed of recognition. Neural networks in combination with computer vision is fast becoming one of the most common uses of AI, as monitoring/safety devices are used in many public places.

Advanced search

Google has been using neural networks in search for ages. Others are adopting it due to its ability to provide answers rapidly and predict requests as a person is typing or speaking. The advantage here is that each node of a neural network is involved with the creation of a response, which sums the power of all the nodes to create a more powerful AI application.

Generative AI

When it comes to generating content, neural networks provide the support needed to provide articles, documentation and papers that will provide a good starting point for a human to then create a valuable piece of content. Neural networks are included in an overall generative AI architecture that enables remarkably fast and very powerful use of a large language model – this LLM is the source, but it’s the neural network that does a key part of the heavy lifting to create generative AI’s output. Specifically, neural networks identify the logical structures within a vast storehouse of data.

As an additional note about neural network use cases, realize that there are many different types of neural networks, of varying capability and scope and focus. But regardless of this enormous variety, all neural networks are applied to solving user problems and making the predictions needed by a wide range of uses cases.

Also see: 100+ Top AI Companies 2023

What is Deep Learning?

Deep learning systems use multiple processing layers to extract progressively better and more high-level insights from data. The key point is the “multiple processing layers,” which enables deep learning software architecture to provide far more robust compute capability.

Deep learning applications can be viewed as a more sophisticated deployment of basic neural networks that make heavy use of machine learning algorithms, are inspired by the human mind, can keep learning from their mistakes and solve highly complex problems.

Machine learning algorithms

Deep learning systems make use of complex machine learning techniques and can be considered a subset of machine learning. But in keeping with the multi-layered architecture of deep learning, these machine learning instances can be of various types and various strategies throughout a single deep learning application.

Human-inspired

The mathematical structures that comprise deep learning have been loosely inspired by the structure and function of the brain. Meaning that humans employ a complex array of variables to make decisions, instead of the usual “on or off” nature of machine computing. The layered mathematical structures in a typical deep learning deployment are an attempt to build a system that can mimic the complexity and nuance of human decision making.

Continuous learning

As they can learn by example and correct their actions based on errors detected, they keep learning and improving their level of accuracy. In the world of artificial intelligence, this is not unique to deep learning; by its very nature, AI is trained and “learns” as it is fed more data, and/or is in active use across time. Consequently, a deep learning application will be much higher performing six months from now than it is now.

High complexity

Deep learning allows machines to tackle problems of similar complexity to those humans can solve.

Thus, deep learning has enabled researchers to scale up the AI models they use in a way that goes well beyond traditional neural networks. By utilizing multiple forms of machine learning systems, models, neural networks and algorithms, deep learning opens many new doors for analysis and problem solving.

“Deep learning can be leveraged to analyze the exceptions when a human intervenes with AI decisions,” said Rick Wagner, Senior Director, Product Management, SailPoint. “Those exceptions can ultimately be analyzed for patterns which ultimately will improve the effectiveness of AI.”

Also see: Generative AI Examples 

Deep learning Use Cases

Deep learning use cases go way beyond those of machine learning and simple neural networks. Machine learning is broadly applicable to a huge range of tasks. As the name implies, deep learning is harnessed to solve problems at a deeper and more complex level. Deep learning is being used to generate text, automatically deliver meeting transcripts, capture data from documents and generate video content from text.

Generate text

Deep learning-based, large language models can generate legible text on various topics or generate realistic images from text prompts. The use of LLM and generative AI application involving text creation is now being widely adopted and therefore is a key driver of deep learning adoption. Deep learning is also being used to provide high-accuracy text transcripts from audio recordings of business meetings and phone calls.

Automatic data capture

Deep learning can be deployed to automatically capture data from business documents with high accuracy. This can be an important part of how deep learning boosts the performance of data analytics, which is happening with increasing frequency in the enterprise. Indeed, while deep learning in data analytics is still on the forward edge, it will certainly gain full saturation as AI gains more adoption.

“Deep learning is being used to automatically capture data from business documents with high accuracy,” said Petr Baudis, CTO and chief AI architect at Rossum. “This can save businesses a lot of time and money, as it eliminates the need for manual data entry.”

Self-driving

Deep learning models are being employed to solve the many challenges inherent in autonomous operation of vehicles. Whether it is self-driving vehicles, vehicle driver assist, obstacle avoidance or equipment that moves around industrial and commercial operations, deep learning is being deployed to ensure safety and improve accuracy.

In sum, deep learning use cases provide multi-faceted answers to complex situations and problems. It elevates traditional machine learning and basic neural networks in terms of scale and depth of analysis.

For more information: AI vs. ML

Bottom Line: Neural Networks vs. Deep learning

There are many similarities between neural networks and deep learning. They each comprise algorithms that are addressed to decode complex challenges.

Deep learning, though, utilizes more sophisticated models than do neural networks and takes longer to set up. Deep learning requires more time to crunch through the much larger data sets and more nuanced problems they typically analyze or address.

As such, deep learning is deployed among a much smaller user base due to the time and cost required to build and run its systems.

In sum, neural networks are now applied across the spectrum of AI applications while deep learning is reserved for more specialized or advanced use cases.

With generative AI very much in the spotlight, it should be pointed out that new applications like ChatGPT and others make heavy use of neural networks. In an increasing number of cases, this involves the very advanced neural networks that can be classified as deep learning. To create content that passes muster, after all, a vast amount of compute resources are required to develop something that is even vaguely comparable to the work of a skilled human.

“When it comes to AI in general, neural networks and deep learning go together, the deeper the learning, the more layers of neurons, the more trained a model can be to enable deployment for different purposes,” said Greg Schulz, an analyst at StorageIO Group. “Think of it as a hierarchy of cognitive computing: basic AI involves relatively simple rules and reasoning; more advanced machine learning adds to the knowledge basis and deep learning takes you to the creation, training and testing of new or enhanced models.”

Also see: Generative AI Startups 

The post Neural Networks vs. Deep Learning appeared first on eWEEK.

• Generative AI is a specific form of AI that is designed to generate content. This content could be text, images, video and music. It uses AI algorithms to analyze patterns in datasets to mimic style or structure to replicate different types of content. It is used to create deepfake videos and voice messages.

• Artificial Intelligence (AI) is a technology that has the ability perform tasks that typically require human intelligence. AI is often used to build systems that have the cognitive capacity to mine data, and so continuously boost performance – to learn – from repeated events.

Let’s more deeply examine generative AI and AI, lay out their respective use cases, and compare these two rapidly growing emerging technologies.

Generative AI vs. AI

Both generative AI and artificial intelligence use machine learning algorithms to obtain their results. However, they have different goals and purposes.

Generative AI is intended to create new content, while AI goes much broader and deeper – in essence to wherever the algorithm coder wants to take it. These possible AI deployments might be better decision making, removing the tedium from repetitive tasks, or spotting anomalies and issuing alerts for cybersecurity.

In contrast, generative AI finds a home in creative fields like art, music and product design, though it is also gaining major role in business. AI itself has found a very solid home in business, particularly in improving business processes and boosting data analytics performance.

To summarize the differences between generative AI and AI, briefly:

• Creativity Generative AI is creative and produces things that have never existed before. Traditional AI is more about analysis, decision making and being able to get more done in less time.
• Predicting the future: Generative AI spots patterns and combines them into unique new forms. AI has a predictive element whereby it utilizes historical and current data to spot patterns and extrapolate potential futures in very powerful ways.
• Broad vs. Narrow: Generative AI uses complex algorithms and deep learning and large language models to generate new content based on the data it is trained on. It is a specific and narrow application of AI to very creative use cases. Traditional AI can accomplish far more based on how the algorithms are designed to analyze data, make predictions and automate actions – AI is the foundation of automation.

Also see: Top Generative AI Apps and Tools

Now, let’s go deeper into generative AI and artificial intelligence:

Understanding Generative AI

Generative AI is AI technology geared for creating content. Generative AI combines algorithms, large language models and neural network techniques to generate content that is based on the patterns it observes in other content.

Although the output of a generative AI system is classified – loosely – as original material, in reality it uses machine learning and other AI techniques to create content based on the earlier creativity of others. It taps into massive repositories of content and uses that information to mimic human creativity; most generative AI systems have digested large portions of the Internet.

Machine learning algorithms

Generative AI systems use advanced machine learning techniques as part of the creative process. These techniques acquire and then process, again and again, reshaping earlier content into a malleable data source that can create “new” content based on user prompts.

Using earlier creativity

As noted above, the content provided by generative AI is inspired by earlier human-generated content. This ranges from articles to scholarly documents to artistic images to popular music. The music of pop singer Drake and the band The Weekend was famously used by a generative AI program to create a “new” song that received considerable positive attention from listeners (the song was soon removed from major platform in response to the musicians’ record label).

Vast datasets

Generative AI can accomplish tasks like analyze the entire database of an insurance company, or the entire record keeping system of a trucking company to produce an original set of data and/or business process that provides a major competitive boost.

Thus, generative AI goes far beyond traditional machine learning. By utilizing multiple forms of machine learning systems, models, algorithms and neural networks, generative AI provides a completely new form of human creativity.

Also see: Generative AI Companies: Top 12 Leaders

Generative AI Use Cases

Generative AI is being used to augment but not replace the work of writers, graphic designers, artists and musicians by producing fresh material. It is particularly useful in the business realm in areas like product descriptions, suggesting variations to existing designs or helping an artist explore different concepts.

Generate text

Generative AI can generate legible text on various topics. It can compose business letters, provide rough drafts of articles and compose annual reports. Some journalistic organizations have experimented with having generative AI programs create news articles. Indeed, many journalists feel the threat from generative AI.

Generate images

Generative AI can generate realistic or surreal images from text prompts, create new scenes and simulate a new painting. Note, however, that the fact that these images are originally based the images fed into the generative AI system is prompting lawsuits by creative artists. (And not only graphic artists, but writers and musicians as well.)

Generate video

It can compile video content from text automatically and put together short videos using existing images. The company Synthesia, for instance, allows users to create text prompts that will create “video avatars,” which are talking heads that appear to be human.

Generate music

It can compile new musical content by analyzing a music catalog and rendering a similar composition in that style. While this has caused copyright issues (as noted in the Drake and The Weekend example above), generative AI can also be used in collaboration with human musicians to produce fresh and arguably interesting new music.

Product design

Generative AI can be fed inputs from previous versions of a product and produce several possible changes that can be considered in a new version. Given that these iterations can be produced in a very short amount of time – with great variety – generative AI is fast becoming an indispensable tool for product design, at least in the early creative stages.

Personalization

Generative AI can personalize experiences for users such as product recommendations, tailored experiences and unique material that closely matches their preferences. The advantage is that generative AI benefits from the hyper-speed of AI – producing personalization for many consumers in mere minutes – but also the creativity it has displayed in art and music to generative fresh, individualized personalizations.

“Generative AI is an indispensable ally for individuals who are newly entering the workforce,” said Iterate.ai Co-Founder Brian Sathianathan. “It can serve as an invisible mentor, assisting with everything from crafting compelling resumes and mastering interview strategies to generating professional correspondence and formulating career plans. By providing personalized advice, learning opportunities, and productivity tools, it can help new professionals navigate their career paths more confidently.”

Also see: AI Detector Tools

Understanding AI

Artificial intelligence is a technology used to approximate – often to transcend – human intelligence  and ingenuity through the use of software and systems. Computers using AI are programmed to carry out highly complex tasks and analyze vast amounts of data in a very short time. An AI system can sift through historical data to detect patterns, improve the decision-making process, eliminate manually intensive task and heighten business outcomes.

Also see: 100+ Top AI Companies 2023

Isolating patterns

AI can spot patterns among vast amounts of data. It does this using specialized GPU processors (Nvidia is a leader in the GPU market) that enable super fast computing speed. Some systems are “smart enough” to predict how those patterns might impact the future – this is called predictive analytics and is a particular strength of AI.

Better business decisions

AI can be used to provide management with possible opportunities for expansion as well as detecting potential threats that need to be addressed. It helps in ways such as product recommendations, more responsive customer service and tighter management of inventory levels. Some executives use AI as an “additional advisor,” meaning they incorporate recommendations from both their colleagues and AI systems, and weigh them accordingly.

Heightened data analytics

AI adds another dimension to data analytics. It offers greater accuracy and speed to the processes of using data analytics. Used correctly, AI increases the chance of success and achieving positive outcomes by basing data analytics decisions on a much wider volume of data – and ideally higher quality data – whether historical or in real time.

Through the rapid detection of data analytics patterns, business processes can be improved to bring about better business outcomes and thereby assist organizations in gaining competitive advantage.

AI Use Cases

AI has almost limitless use cases – and more seem to crop up every week. Some of the top AI use cases include automation, speed of analysis and execution, chat and enhanced security. Be aware the additional vertical use cases are launching in education, healthcare, finance and other industry sectors.

Automation

AI can automate complex, multi-step tasks to help people get more done in a shorter span of time. For instance, IT teams can use it to configure networks, provision devices, and monitor networks far more efficiently than humans. AI is the driver behind robotic process automation, which helps office workers automate many mundane tasks, freeing up humans for higher value tasks.

Speed

AI finishes tasks with extraordinary speed. It uses technologies like machine learning, neural networks and deep learning to find and manipulate data in a very short time frame. This helps organizations to detect and respond to trends and opportunities in as close to real time as possible. The amount of data AI can analyze lies far outside the range of rapid inspection by a person.

Chat

AI-based chat, and the chatbots it powers, appears to be the app that has finally taken AI into the mainstream. Systems such as ChatGPT and others are introducing chat into untold numbers of applications. Done well, these applications improve customer service, search and querying, to name a few. And the advantage of AI is that, over time, the system improves, meaning that the AI chatbot is capable of ever more human conversation.

Enhanced Security

AI harnesses machine learning algorithms to analyze, detect, and alert managers about anomalies within the network infrastructure. Some of these algorithms attempt to mimic human intuition in applications that support the prevention and mitigation of cyber threats. This can help to alleviate the work burden on understaffed or overworked cybersecurity teams. In some cases, AI systems can be programmed to automatically take remediation steps following a breach.

AI, therefore, is finding innumerable use cases across a wide range of industries. It provides managers with data and conclusions they can use to improve business outcomes. Moreover, AI technology in all of its forms is still in its infancy, so expect the application of AI to uses cases to both broaden and deepen.

Also see: Best Artificial Intelligence Software 2023

Bottom Line: Generative AI vs. AI

Algorithms can be regarded as some of the essential building blocks that make up artificial intelligence. AI uses various algorithms that act in tandem to find a signal among the noise of a mountain of data and find paths to solutions that humans would not be capable of. AI makes use of computer algorithms to impart autonomy to the data model and emulate human cognition and understanding.

Generative AI is a specific use case for AI that is used for sophisticated modeling with a creative goal. It takes existing patterns and combines them to be able to generate something that hasn’t ever existed before. Because of its creativity, generative AI is seen as the most disruptive form of AI.

“Mainline AI applications based around learning, training and rules are fairly common in support of autonomous operations (vehicles, drones, control systems) as well as diagnostics, fraud and security detection, among other uses,” said Greg Schulz, an analyst at StorageIO Group. “Generative AI has the ability to ingest large amounts of data from various sources that gets processed by large language models (LLMs) influenced by various parameters to create content (articles, blogs, recommendations, news, etc.) with a human-like tone and style.”

Also see: ChatGPT vs. Google Bard: Generative AI Comparison

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• GPT4 is the latest version of ChatGPT, the popular AI-based chatbot that can also be used to generate basic computer code. It was developed by OpenAI and is now incorporated into Bing and other Microsoft products.
• ELMAR is a large language model (LLM) that enables businesses to create sophisticated chatbots. Developed by Got It AI, it emphasizes secure, on-premises AI and is carving out a specific niche where it looks to be competitive with ChatGPT and others. Yet ELMAR is even newer than ChatGPT in the marketplace.

Let’s examine the similarities and the differences between GPT4 and ELMAR across a range of different criteria.

Also see: Top Generative AI Apps and Tools

And: 100+ Top AI Companies 

Quick Comparison: GPT vs. ELMAR

GPT4 vs. ELMAR: Feature Comparison

GPT is an abbreviation for Generative Pre-trained Transformer, a form of advanced artificial intelligence (AI). GPT4 simulates thought by using a neural network machine learning model trained based upon a vast trove of data gathered from the internet.

The ChatGPT AI-powered language model was developed by OpenAI. It was trained to be able to generate human-like text responses to a given prompt. It answers questions, can converse with users on a variety of topics, and even generate creative writing pieces.

As such, GPT4 goes far beyond being a chatbot. It can create documents and articles and solve problems. GPT4 can also do image interpretation using multimodal language AI models. This enables it to build websites based on sketches, and suggest recipes based on a photo.

GPT4 can perform complex tasks. It has achieved some success with basic computer programming duties, but it ventures well beyond that into territory such as drawing up simple lawsuits, creating elementary computer games, passing exams, checking for plagiarism, generating written content, summarizing documentation, highlighting key passages within text and translating dozens of languages.

ELMAR is short for Enterprise Language Model Architecture. It styles itself as an enterprise-ready language model that enables businesses to create sophisticated chatbots that can communicate with customers or internal stakeholders in a natural and engaging way.

It is designed to be fine-tuned for specific use cases. The latest iteration includes a TruthChecker which is a post-processor that helps to compare responses generated by other language models to flag potentially incorrect, misleading or incomplete answers and AI hallucinations.

There are also preprocessors that add more security and can filter out unwanted data or mask personal data. ELMAR is aimed at specific and narrowly defined data sets. It has been used successfully with the knowledge bases of Zendesk and Confluence. Hence, it doesn’t try to compete with GPT4 as a way to “analyze the internet.”

GPT4 wins on breadth of features and capabilities, as it is far more than a chatbot.

On a related topic: What is Generative AI?

GPT4 vs. ELMAR: Chatbot Functions

GPT4 is used a lot in chatbot applications to automate customer service, answer FAQs, and engage in conversation with users. It can respond conversationally by tapping into a comprehensive set of online text, as well as news items, novels, websites, and more.

Overall, GPT4 does a good job of analyzing information, evaluating online behavior, and even making product recommendations as part of the online sales and upselling process. Automation features extend to appointment scheduling, reservations, payment processing, queries about shipping schedules, order progress, product returns, product and service availability, and more, with a good level of accuracy.

ELMAR can be looked upon as a much more focused chatbot than GPT4. It can be fine-tuned to produce task-specific custom models that reside on-premises for security or data privacy purposes. Further, it doesn’t need to use third-party APIs, which provides additional enhancement to the security posture and eliminates the possibility of a surge in inference costs, which can happen on other AI models. ELMARs’ pre-processing and post-processing features catch and filter out wrong answers and hallucinations.

GPT4 wins as a broad chatbot. ELMAR wins for on-premises and highly security-conscious use cases.

Also see: Generative AI Companies: Top 12 Leaders

GPT4 vs. ELMAR: Accuracy of Response

GPT4 can be prone to error based on assumptions made on data that may not be current. But most of the time it is accurate. It got into hot water with a few strange responses to queries and one or two completely wrong answers.

Fortunately, each new version gets better. GPT4 added a greater degree of accuracy over ChatGPT. OpenAI stated that GPT4 is 82% less likely to respond to requests for content that OpenAI does not allow than its predecessor, and 60% less likely to invent answers. But don’t expect it to be perfect. That includes coding. Its programming output should always be verified by human eyes.

Accuracy is a big advantage of ELMAR. Many large language models hallucinate and cannot be trusted for mission-critical applications. ELMAR surrounds the core LLM model with pre-processing and post-processing features to minimize unpredictability and inaccuracy. Its TruthChecker, for example, can spot problematic or incorrect responses. When used, it takes the hallucination rate of ELMAR from 14% to 1.4%. Per numbers from Got It AI, GPT4 has a hallucination rate of 8.4%. These accuracy features enhance the reliability of chatbot interactions.

ELMAR wins on accuracy.

Also see: ChatGPT vs GitHub Copilot

GPT4 vs. ELMAR: Integration

GPT4 comes out of the open source community. It can be plugged into other applications to generate responses via an API. Plugins are becoming available, including those for the likes of Kayak, Expedia, OpenTable, Slack and Shopify, with more on the way. It is also integrated with a lot of different programming languages.

The ELMAR LLM can be integrated with any knowledge base for dialog-based chatbot Q&A applications but is not integrated with that vast database known as the internet.

Both integrate well, but perhaps GPT4 has much broader integration capabilities across the cloud and the web. ELMAR wins for localized integration on-premises.

Also see: Generative AI Startups

GPT4 vs. ELMAR: Security

GPT4 hasn’t really had much attention on security to date. Developers using it are expected to build in their own security features.

ELMAR, on the other hand, is all about security and data privacy. Those deploying it can take advantage of many features to secure their language model architecture against attacks. Its models are deployed within the enterprise security envelope and are under the full control of the business. There is no going to the cloud or the web for answers and potentially exposing applications to attack.

Users can also set policies to remove data such as personally identifiable information (PII) so that unauthorized internal users only get to see the information they need. For those that need AI chat but need to avoid information exposure by linking to third-party applications, ELMAR is a good solution.

ELMAR is the clear winner on security and data privacy.

For more information, also see: Top AI Startups

GPT4 vs. ELMAR: Pricing

GPT4 has a basic version available for free but the main ChatGPT Plus version costs roughly $20/month. Those subscribing gain access to ChatGPT at peak times, faster responses, and priority access to new features and improvements.

On top of the basic subscription, there is a pricing scale per 1,000 tokens (chunks of words). 1,000 tokens comes out to about 750 words of material. Costs range from 3 cents to 6 cents per 1,000 tokens for prompts, and another 6 to 12 cents per thousand once finished. The higher rate provides access to a larger set of contextual data.

Unfortunately, ELMAR pricing isn’t publicly available. The company does say that it runs using commodity hardware. Not only does this ensure that sensitive data does not leave the premises, it is also said to keep costs low. But there are no specifics available. In any case, ELMAR remains in the testing phase and so an announcement about pricing should come once it is broadly released.

GPT4 wins on pricing until such times as ELMAR releases its rates.

On a related topic: Top Natural Language Processing Companies

GPT4 vs. ELMAR: Bottom Line

GPT4 uses a transformer-based architecture as part of a neural network that handles sequential data. Although the data it draws from may be a little dated at times, GPT4 seems to perform okay in coding, and does a very good job on chat, language translation, answering questions, and understanding images. It can even determine why a joke is funny.

Overall, ELMAR offers an attractive value proposition for businesses looking to leverage an AI chatbot that is secure and keeps data private. As it runs on commodity hardware internally and doesn’t use third-party APIs, it looks like a no-brainer to organizations with highly sensitive data and strict security needs. But remember that ELMAR is still in the testing phase. Got It AI is running enterprise pilots, which are being used to improve ELMAR’s speed, accuracy and cost-effectiveness. So, it has a way to go before it can be considered a viable alternative to GPT4.

Also see: What is Artificial Intelligence?

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• Machine learning is a technique used to help computers learn using training that is modeled on results gleaned from large data sets.
• Deep learning is a form of machine learning based on artificial neural networks that are modeled after the native capabilities of the human brain. It can be viewed as “machine learning on steroids” as it takes the basic capabilities of machine learning to a higher level.

For more information, also see: Best Machine Learning Platforms

How Machine Learning Compares to Deep Learning

Machine learning is highly advanced technology; some of the tasks that can be done with it seem miraculous. Deep learning is still more complex but has a more limited set of applications. It typically requires more time and resources to set up and analyze but provides deeper and better conclusions. In contrast, machine learning solutions can often be arrived at faster as they are more narrowly defined and apply to a smaller data set.

Deep Learning Takes More Time

As deep learning platforms take time to analyze data sets, it typically take far longer to set up and longer to reveal its results. More compute and processing power is usually involved.

Machine Learning Can be More Specific and Faster

Machine learning algorithms can be unleashed on a specific issue to solve or improve it rapidly. Because of this, machine learning has become a very common enterprise use of artificial intelligence.

Deep Learning Goes Deeper

Machine learning can sift through data to spot patterns while deep learning can analyze a much larger data set and detect more subtle patterns of anomalies.

Deep Learning is “Smarter”

Deep learning is better able to learn from mistakes and adapt to do better next time.

Machine learning and deep learning are both incredibly valuable tools in assisting humans in addressing problems and in removing the burden of repetitive manual labor. Both will play a role in the development of a more intelligent future applications.

Also see: Generative AI Companies: Top 12 Leaders 

How Does Machine Learning Work?

Machine learning users computerized systems that can learn and adapt automatically without the need for continual instruction. Once set up, the system applies itself to a dataset or problem, spots situations or solves problems. Machine learning can draw inferences, address complex problems and solve them automatically.

Draws Inferences

Machine learning is based on algorithms and statistical models that analyze and draw inferences from patterns discovered within data.

Creates Automatic Solutions

Algorithms are procedures designed to automatically solve well-defined computational or mathematical problems or to complete computer processes.

Solves Complex Problems

Algorithms go beyond computer programming as they require understanding of the various possibilities available when solving a problem.

Machine learning algorithms, then, can be regarded as the essential building blocks of modern AI. Machine learning finds a pattern or anomaly amongst the noise of data and finds paths to solutions within a time frame that humans would not be capable of. They also help impart autonomy to the data model and emulate human cognition and understanding.

Also see: Top Generative AI Apps and Tools

How Does Deep Learning Work?

Deep learning systems use multiple processing layers to extract progressively better and more high-level insights from data. It can be viewed as a more sophisticated application of machine learning that makes heavy use of machine learning algorithms, are inspired by the human mind, can keep learning from their mistakes and solve highly complex problems.

Use Machine Learning Algorithms

Deep learning systems use standard machine learning techniques and can be considered a subset of machine learning. Yet it is almost always more sophisticated than machine learning, in terms of its problem solving ability.

Is Human-Inspired

The mathematical structures that comprise deep learning have been loosely inspired by the structure and function of the brain. Meaning it can handle more nuance and come closer to what humans think of as creativity.

Enables Continuous Learning

Since deep learning applications can learn by example and correct their actions based on errors detected, they keep learning and improving their level of accuracy.

Contains High Complexity

Deep learning allows machines to tackle problems of similar complexity to those humans can solve.

Thus, deep learning has enabled researchers to scale up the models they use in a way that goes well beyond traditional machine learning. By utilizing multiple forms of machine learning systems, models and algorithms, deep learning opens new doors for analysis and problem solving.

Also see: What is Artificial Intelligence?

Machine Learning Use Cases

Machine learning has a great many use cases. In fact, machine learning has crept into just about every conceivable area where computers are used. For example, it is used in analytics, rapid processing, calculations, facial recognition, cybersecurity, and human resources.

Drives Analytics

Data analytics systems are made faster and smarter by harnessing machine learning. At this point, most all enterprise data analytics applications incorporate machine learning.

Performs Calculations

Just as pocket calculators largely replaced manual addition and multiplication, machine learning takes care of mathematical calculations of almost infinite proportions.

Enables Facial Recognition

Machine learning algorithms can find an identity among millions of candidates as part of facial recognition systems.

Assists Cybersecurity

Machine learning is now part and parcel of network monitoring, threat detection and cybersecurity remediation technology.

Supports Human Resources (HR)

When incorporated into recruitment tools, machine learning brings about more efficient tracking of applicants, analysis of employee sentiment, overall productivity and can speed the hiring process.

Machine learning, therefore, is employed to find needles in haystacks consisting of massive quantities of data. It ties into big data in that these algorithms can be utilized to scan structured and unstructured data, and social media feeds.

For more information, also see: Top AI Software 

Deep Learning Use Cases

Deep learning use cases go beyond those of machine learning. Machine learning is broadly applicable to a huge range of tasks. As the name implies, deep learning is harnessed to solve problems at a deeper and more complex level. Deep learning is used to generate text, automatically deliver meeting transcripts, capture data from documents and generate video content from text.

Generate Text

Deep learning-based, large language models can generate credible and in-depth text on various topics or generate realistic images from text prompts.

Create Transcripts

Deep learning is being used to provide high-accuracy text transcripts from audio recordings of business meetings and phone calls.

 Automatically Capture Data

Deep learning can be deployed to automatically capture data from business documents with high accuracy.

Produce Video Content

Another use case that is emerging is to generate video content from text automatically. In this case, the video often uses virtual avatars for the onscreen speakers.

Deep learning use cases provide multi-faceted answers to complex situations and problems. It elevates machine learning in terms of scale and depth of analysis.

On a related topic: Top Natural Language Processing Companies

Bottom Line: Machine Learning vs. Deep Learning

In many ways, machine learning and deep learning can be viewed as cousins, if not siblings. They each comprise algorithms that are addressed to complex challenges. Deep learning, though, utilizes more sophisticated models that take longer to set up and require more time to crunch through the larger data sets they typically analyze.

As such, deep learning is in use among a much smaller user base due to the time and cost required to build and run its systems.

But as time goes on, the necessary investment is diminishing. Perhaps within a year or two, the separation between machine learning and deep learning will become a moot point. The technology could advance to the point where deep learning techniques become so accessible that they begin to be applied broadly to problems that are currently the province of more limited machine learning algorithms.

On a related subject: Algorithms and AI

The post Machine Learning vs. Deep Learning appeared first on eWEEK.

Advanced AI Chatbots – in essence, generative AI platforms – are applications designed to address online chat functions using text or speech. By adding advanced artificial intelligence (AI) and machine learning algorithms to online chat, text and image creation, accuracy and responsiveness are enhanced.

When comparing ChatGPT and Watson Assistant, which is best? Let’s examine the similarities and the differences between these generative AI bots across a range of different criteria.

Also see: Generative AI Companies: Top 12 Leaders 

And: ChatGPT vs. Google Bard: Generative AI Comparison

ChatGPT vs. Watson Assistant: Quick Comparison

On a related topic: What is Generative AI?

ChatGPT vs. Watson Assistant: Feature Comparison

GPT is an abbreviation for Generative Pre-trained Transformer, a form of advanced artificial intelligence (AI). Using a neural network, ChatGPT simulates thought using a machine learning model trained based upon a vast trove of data gathered from the internet.

The ChatGPT is a generative AI language model was developed by OpenAI. It was trained on a massive amount of text data from the internet to generate human-like text responses to a given prompt. It answers questions, can converse with users on a variety of topics, and even generate creative writing pieces.

As such, ChatGPT goes far beyond being a chatbot to being able to create documents, and articles, and solve problems. ChatGPT is a far broader offering than Watson.

Watson Assistant is much more focused as an offering. Using artificial intelligence, this AI-chatbot platform developed by IBM enables businesses to build, train, and deploy conversational interactions across web, mobile, messaging platforms, and other channels. It offers personalized customer experiences and is designed specifically for customer service, technical support, and as a virtual assistant.

Watson Assistant, then, is best for those building custom chatbots for businesses. ChatGPT should be looked upon as more of a language-based AI model that can enhance the conversational capabilities of chatbots.

In reality, then, they are not really direct competitors.

Also see: Generative AI Startups

ChatGPT vs. Watson Assistant: Chatbot Functions

ChatGPT is used a lot in chatbot applications to automate customer service, answer FAQs, and engage in conversation with users. It can respond conversationally by tapping into a comprehensive set of online text written by actual people, as well news items, novels, websites, and more.

Watson is a narrower chatbot app that is highly customizable to specific verticals and use cases. IBM provides pre-built components to use in areas such as flight and other reservations and answering FAQs.

As a chatbot, Watson rigorously sticks to what it is programmed to respond to and never strays from that. If it doesn’t know, it will refer to an agent.

Due to this, Watson is a more reliable chatbot application than ChatGPT, though not nearly as capable in a broader sense. IBM has been integrating more conversational AI features into Watson Assistant, but it is far behind ChatGPT in that regard.

ChatGPT vs. Watson Assistant: Marketing and Sales Support

ChatGPT does a good job of analyzing information, evaluating online behavior, and making product recommendations as part of the online sales and upselling process. Automation features extend to appointment scheduling, reservations, payment processing, queries about shipping schedules, order progress, product returns, product and service availability, and more with a high level of accuracy.

Watson Assistant has long years of experience in providing customer service to verticals such as insurance, finance, and healthcare where it is programmed to understand industry-specific language. It also does a good job of tech support.

For specific products and services, Watson harnesses a database all about those tools and services, and ably lowers the call burden on call centers teams while providing users with the information they need. It is also set up to guide customers through a sales funnel, offer product recommendations, and pass on leads to sales.

Watson Assistant wins here, too.

On a related topic: The AI Market: An Overview

ChatGPT vs. Watson Assistant: Image Interpretation

It is the image interpretation category that really sets ChatGPT apart. The latest version, known as GPT-4, is a multimodal language AI model that interprets images. As a result, it can build websites based on sketches, and suggest recipes based on a photo of what is in the fridge or sitting on a countertop.

Watson Assistant can’t do any of that. ChatGPT is the clear winner in this category. Expect IBM to add such features in the future, though.

ChatGPT vs. Watson Assistant: Accuracy of Response

ChatGPT can be prone to error, based on assumptions made on data that may not be current. But most of the time it is accurate.

ChatGPT got into hot water with a few strange responses to queries and one or two completely wrong answers. Fortunately, each new version gets better. GPT-4 added a greater degree of accuracy. OpenAI stated that GPT-4 is 82% less likely to respond to requests for content that OpenAI does not allow than its predecessor, and 60% less likely to invent answers. But don’t expect it to be perfect.

Watson Assistant will make even fewer goofs as it addresses a tiny subset of the total data that ChatGPT covers. ChatGPT might do better at attempting to explain the meaning of life, but Watson Assistant is more likely to recommend the right product upgrade or technical support action.

On a related topic: The Future of Artificial Intelligence 

ChatGPT vs. Watson Assistant: Integration

Watson Assistant can integrate with back-end systems, and a great many CRM, voice assistants, knowledge management, and other enterprise systems. IBM’s long years of experience in the IT community mean that it has the deep relationships needed for far reaching integration.

ChatGPT comes out of the open-source community and lacks the commercial relationships of IBM. As such, it is not nearly as advanced on the integration front. But it can be plugged into them to generate responses via an API. Plugins are becoming available. So far, there plugins are ready for applications such as Kayak, Expedia, OpenTable, Slack and Shopify, with more on the way.

ChatGPT vs. Watson Assistant: Complex Tasks

The greater the complexity of the task, the more ChatGPT comes into its own. It has achieved success with basic computer programming, in drawing up simple lawsuits, in creating elementary computer games, and in passing exams.

In the Biology Olympiad test, for example, ChatGPT scored in the 99th percentile. Further capabilities include an AI Text Classifier, which is a plagiarism checker. It has gotten quite good at distinguishing between AI-written and human-written text, and in the detection of automated misinformation campaigns that take advantage of AI tools.

Users are warned, however, that the general limitations of both AI chatbots include a higher likelihood of inaccuracy with texts below 1,000 characters, and that plagiarism results are better with English than other languages.

Content generation is another of the complex tasks that ChatGPT can accomplish. It can produce text that can use the same style and grammar as an original piece of material, summarize long texts and reports to reflect the primary ideas with some accuracy, highlight key passages within text, and translate into dozens of languages.

Watson Assistant can address complexity within the narrow bounds of product information about a certain number of products for a specific vendor or market niche. It doesn’t stray from those parameters. ChatGPT wins as the complexity of the required tasks are magnified.

Also see: ChatGPT: Understanding the ChatGPT ChatBot 

ChatGPT vs. Watson Assistant: Security

Watson Assistant, as would be expected due to its use in large enterprises, provides a robust set of security features. It has integrated user authentication and access controls, and offers comprehensive encryption. It complyies with a wide array of standards and regulations.

ChatGPT, however, hasn’t really had much attention on security to date. Developers using it are expected to build in their own security features.

ChatGPT vs. Watson Assistant: Pricing

ChatGPT has a basic version available for free but the main ChatGPT Plus version costs roughly $20/month. Those subscribing gain access to ChatGPT at peak times, faster responses, and priority access to new features and improvements.

On top of the basic subscription, there is a pricing scale per 1,000 tokens (chunks of words). 1,000 tokens comes out to about 750 words of material. Costs range from 3 cents to 6 cents per 1,000 tokens for prompts, and another 6 to 12 cents per thousand once finished. The higher rate provides access to a larger set of contextual data.

Watson Assistant has a lite version available free, which might work for home or mobile users. For $140 a month, you gain phone and SMS integration, and must pay extra for heavy usage beyond a particular threshold.

For more information, also see: Top AI Startups

ChatGPT vs. Watson Assistant: Bottom Line

ChatGPT uses a transformer-based architecture as part of a neural network that handles sequential data. Although the data it draws from may be a little dated at times, ChatGPT seems to perform respectably in chatbots, and does a very good job on language translation, answering questions, understanding images, and can even determine why a joke is funny. But ChatGPT is not so much a chatbot as an AI-based system that can be incorporated into other chatbot applications.

Watson Assistant, on the other hand, can be regarded as a narrower but more targeted version of ChatGPT that is precisely aimed at the chatbot market. It lacks the contextual understanding that ChatGPT can bring to the table but is a lot more focused and generally better as a chatbot.

Watson Assistant is also not influenced by hate speech or misinformation, unlike ChatGPT at times. And it does better at knowing when it needs to turn things over to a human agent. In addition, Watson Assistant is good at remembering the data it is trained on and thus, in not straying far from what it is supposed to do.

In sum, Watson Assistant is best for purely chatbot actions whereas ChatGPT should be regarded as a more general purpose AI technology that can enhance the chatbot function.

However, the added cost of Watson Assistant means that it is never going to achieve the widespread use that ChatGPT is likely to have. It will remain a niche product used where the economics of customer service and sales make sense. ChatGPT is destined for much broader usage.

On a related topic: Top Natural Language Processing Companies

The post ChatGPT vs. Watson Assistant appeared first on eWEEK.

However, there are as many differences as similarities between these two solutions. In sum, they’re very different products that will appeal to buyers with different goals in mind. Here’s a look at both, how they compare, and their ideal use cases.

For more information, also see: Best Data Analytics Tools 

Datadog vs. Splunk: Key Feature Comparison

The Splunk platform enables searching, network monitoring, and analyzing a vast amount of IT data to identify data patterns, provide metrics, diagnose problems and aid in business and IT decision making.

To understand the scope of Splunk: Security Information & Event Management (SIEM) can be considered just one small part of its feature arsenal. Beyond security, it takes in APM, compliance, automation, orchestration, forensics, as well as plenty of features related to IT service management (ITSM) and IT operations management (ITOM).

Splunk’s wide range of products and features are aggregated within the Splunk Observability Suite. The platform can be used to analyze, ingest, and store data for later use, as well as detect issues impacting customers. Overall, it offers a breadth of management that spans all of IT and security. Those wishing to manage SIEM, ITOM and ITSM in an integrated fashion will find Splunk to be a fine tool that can do the job. It offers plenty of real-time visualization and analysis features, as well as management and monitoring.

Datadog stops short of calling itself a complete SIEM, ITSM or ITOM platform. It is more focused on cloud monitoring and security. It offers the ability to see inside any stack or application at any scale and anywhere. Infrastructure monitoring, APM, log management, device monitoring, cloud workload monitoring, server monitoring, and database monitoring fall within its feature set.

Datadog is particularly astute at dealing with the performance and visibility of multiple clouds operating on the network and in managing cloud services. Datadog helps IT to drill down into performance data. It generates alerts about potential problems and helps IT to discover any underlying issues.

Datadog can assemble data from logs and other metrics to provide context that is helpful in minimizing incident response time. The user interface centralizes performance monitoring, alert management, and data analysis in one place. Recent additions to its platforms include network monitoring, security analysis, AIOps, business analytics, a mobile app, and an incident management interface.

Delving deeper into both tools, the best way to differentiate them is how they operate. The Splunk application takes more of a log management approach, which makes it ideal for managing and monitoring the large amount of data generated from the devices running on the network. Datadog, on the other hand, takes more of a monitoring approach geared toward analytics. Thus, Datadog tends to be favored by DevOps and IT teams to address cloud and infrastructure performance.

Splunk wins on breadth of features while Datadog wins slightly in terms of APM depth.

For more information, also see: Top Data Mining Tools

Datadog vs. Splunk: New Features

Both companies have been active with new features and updates, with Datadog being by far the most frequent when it comes to product announcements. These include integration with Amazon Security Lake to make it easy for Amazon Security Lake users to send cloud security logs to Datadog in a standard format. This eliminates the need to build data pipelines to aggregate and route security logs to various security analytics solutions.

Datadog makes this possible via minimal configuration requirements. Once security logs are ingested, users can analyze and identify threats through out-of-the-box detection rules or by writing custom security rules.

In addition, Datadog has released Universal Service Monitoring, which automatically detects all microservices across an organization’s environment and provides visibility into their health and dependencies without any code changes. This complements Datadog’s existing infrastructure monitoring and application monitoring capabilities.

Finally, Datadog has released Cloud Cost Management to show an organization’s cloud spend in the context of observability data. This allows engineering and FinOps teams to automatically attribute spend to applications, services, and teams, track any changes in spend, understand why those changes occurred and include costs as a key performance indicator of application health.

Splunk’s announcements have tended to focus on financials, highlighting its position as an established player in the market that is well ahead of Datadog in the revenue stakes. But there have been a few recent product and service updates.

Splunk extended its collaboration with Amazon Web Services (AWS), with whom it is named the ISV Partner of the Year in North America. It, too, has released the Splunk Add-on for Amazon Security Lake to the Splunkbase content marketplace. This enables the creation of a security data lake from integrated cloud and on-premises data sources as well as from private applications. Joint Splunk and AWS customers can benefit via simplified sharing and analyzing of disparate security data by eliminating the step of normalizing the data first.

Datadog wins on new features and innovation.

To learn more, also see: Top Business Intelligence Software 

Datadog vs. Splunk: Management, Support, and Ease of Use

Splunk’s wide range of products and features are aggregated within the Splunk Observability Suite. The platform can be used to analyze, ingest, and store data for later use, as well as detect issues impacting customers.

Overall, Splunk offers a breadth of management that Datadog doesn’t attempt to rival. Those wishing to manage all security information and events (SIEM), all IT operations (ITOM), or all IT services (ITSM) will find Splunk far more complete by far than Datadog. There is no question that Splunk spans a lot more of the IT landscape than Datadog.

Thus, there are advantages for those that choose Splunk. For example, Splunk offers a wealth of real-time visualization and analysis features that Datadog cannot compete with. If real-time management and monitoring are vital, then this one is a no contest.

Splunk, however, isn’t easy to implement, according to user reports. Initial deployment can be accomplished via the cloud. Due to the size and complexity of Splunk, it isn’t for beginners. It requires a higher level of skilled internal resources as well as vendor support to deploy and operate. Users report that the sophistication of Splunk is mirrored in ease of use. Those very familiar with the platform will find it relatively easy to run. Everyone else has a steep learning curve.

Datadog installation, in contrast, is said to be straightforward, courtesy of the deployment of agents. But some command line scripting is required. It is relatively easy to customize dashboards and interfaces to the way you want them. The main interface covers a lot of ground. Great for experienced users, but it might be tough for new users who may be overwhelmed by the number of options.

Whereas Splunk wins hands down on breadth of management, Datadog comes out ahead on depth – at least across a limited feature set. Purely within APM and cloud services, Datadog offers better drill down and general management capabilities. Further, it is better at managing itself. Whereas Splunk relies on IT to notice and troubleshoot issues related to Splunk, Datadog generates alerts about potential or actual problems within itself and helps IT to identify the underlying issues.

This one is a split decision.

Also see: What is Data Visualization

Datadog vs. Splunk: Pricing

It is well known that Splunk isn’t a low-cost option. Once it ascended to become the darling of SIEM and ITSM a few years ago, it set its prices accordingly. The various modules within Splunk also have a reputation for being expensive.

Further, upselling can send the budget much higher i.e., if you want the SIEM module. If you need performance monitoring, that adds in an APM module, and slowly other modules creep in and the price tag rises. This is normal enough in IT. But when you are already dealing with a pricey platform, it is important to determine what you really need and what you can dispense with.

For example, Splunk offers a wealth of real-time visualization and analysis features that Datadog does not. If real-time management and monitoring are vital, then Splunk is the clear choice. But it does come at a price.

Real-time monitoring sounds great, but not everyone needs it enough for to pay this price premium. Datadog skips real-time and is quite a bit cheaper than its big rival. As for deployment, and support, Datadog also comes out well ahead in terms of keeping costs down. Splunk implementation and support costs can escalate as the software is rolled out.

Also see: Real Time Data Management Trends

Datadog vs. Splunk: Bottom Line

Splunk and Datadog are both excellent tools designed to solve a great many challenges related to security and performance monitoring. You can’t go wrong too far wrong with either one. Both are strong in APM. In fact, both are regarded as leaders in the latest Gartner APM Magic Quadrant. Both also offer a lot of advanced features for your money that go far beyond APM. And both are trailblazers when it comes to innovation and future roadmaps.

In reality, though, it isn’t a case of one versus the other so much as it is a case of determining what you really need. Datadog is all about performance measurement for cloud services and is particularly adept at measuring the performance of databases and servers and measuring performance in a multi-cloud world. It doesn’t attempt to embrace the entire SIEM, ITOM, ITSM spectrum. Rather it takes one slice and does that portion really well. Those that have already deployed plenty of tools for security and IT management, therefore, may gravitate more toward Datadog to supplement ongoing efforts.

Splunk, however, is a much broader platform and toolset geared for a heavy duty large enterprise. Its log management approach often proves invaluable in rapidly analyzing log files and making sense of mountains of data so that IT knows what is going on. Whether it’s a performance slowdown or a security incursion, Splunk is a good way to stay one step ahead of trouble. Those needing an all-encompassing security and IT management platform, therefore, will find Splunk closer to their needs. Additionally, those with aging applications that are ready for a major management makeover will find Splunk a good fit. It covers a large amount of ground – if you have the budget for it.

For more information, also see: What is Data Governance

The post Datadog vs Splunk: APM Software Comparison appeared first on eWEEK.

The term generative AI entered the public consciousness like a whirlwind in November 2022, when OpenAI debuted ChatCPT. Stories about AI chatbots and generative AI were all over social media news outlets. This emerging technology was going to change the world and make whole industries obsolete, or so the news suggested. 

Google soon debuted its own generative AI with the launch of Google Bard. And while the debut event wasn’t perfect – Google Bard made an factual error – the AI platform’s potential is enormous. Google has vast expertise in algorithms and artificial intelligence and so it’s reasonable to forecast that Bard will develop rapidly. 

Which generative AI tool is best? There are similarities between ChatGPT and Google Bard but there are also differences. Let’s compare and evaluate them.

Note: Google’s Bard is now called Gemini. To read our in-depth comparison that focuses on the new Gemini platform, see Gemini vs. ChapGPT: AI Apps Head-to-Head

ChatGPT vs. Google Bard: Key Features

ChatGPT is the most recent iteration of natural language processing models. Its GPT-3 language model has been trained at length using online text written by actual people, as well as news items, novels, websites, and many more sources. It uses this database to create responses to queries. And the ChatGPT AI-powered chatbot takes advantage of machine learning to respond conversationally. 

ChatGPT versions range from more than 100 million parameters to as many as six billion to churn out real-time answers. It includes AI Text Classifier, which is a plagiarism checker.

ChatGPT has already gravitated to the Microsoft side of the cloud computing market due to its relationship with Bing. But that isn’t to say that other hyperscalers and big tech giants won’t employ it – or develop their own alternatives.

Enter Google Bard, which has been around as an experimental language model since the middle of 2021. Google runs it on top of its BERT AI language model as a way to answer questions, conduct sentiment analysis, and perform language translation. Its answers go far beyond those typically given during a traditional Google search. 

While ChatGPT can manage up to six billion parameters, Bard tops out at 1.6 billion.  It also lacks a plagiarism checker. On the plus side, Bard has the Google search engine universe to glean data from whereas ChatGPT has to rely on the less comprehensive Bing search engine.

As Google Bard uses a smaller model than ChatGPT, it requires a smaller amount of computational power. For those with limited computing resources, this gives a clear advantage to Google.

Category Winner

Chat GPT wins on overall feature set, but Google Bard wins on computational resources. And don’t expect Google to accept overall second place. It is throwing a lot of resources at the AI competition. 

On a related topic: Top Natural Language Processing Companies

Also see: The Future of Artificial Intelligence

ChatGPT vs. Google Bard: Content Generation

ChatGPT can summarize long texts, articles, and reports to reflect the primary ideas with some accuracy. It can also highlight key passages within text.

However, it may struggle with long and detailed texts. I can’t imagine it would do well summarizing the Old Testament or the Buddhist texts such as the Bhagavad-Gita, which combine many concepts together. As for requesting in-depth analysis of poetry, early reports suggest that it’s not ChatGPT’s strong suit.

It is apparently possible to train ChatGPT to produce text that can mimic the style and grammar as an original piece of material. This is probably good for consistency in social media posts and email marketing. 

A big strength of ChatGPT is translation of English into other languages. It has already offered improvements in translations. The next time you see a Chinese post in Facebook, translate it if you want a laugh. Often the translations are awful. Improvement in this area is much needed and ChatGPT is a definite step forward.

Google Bard lacks many of these features. But it is supposedly better at creative writing via its ability to offer thematic, word, and phrasal suggestions that are designed to help writers come up with ideas. Being a writer myself, I’m doubtful of this capability but we will see.

Perhaps the best use of Google Bard for writers might be to provide feedback on aspects such as setting, style, tone, and overall structure, as well as making it easy to find synonyms, alternative phrasings, and to spot overused words and terms.

Category Winner

Currently, ChatGPT wins on content generation.

On a related topic: The AI Market: An Overview 

ChatGPT vs. Google Bard: Search Engine Integration

You would think that Google would lead in this category. But it fumbled in its launch launch and demonstration of Bard and has fallen behind in search engine integration. In any case, Google could be accused of resting on its laurels to some degree on search. Arguably, Google’s 93% global market dominance of search is under threat with the appearance of AI-enhanced search.

ChatGPT, meanwhile, has surpassed the 100 million user mark and is already integrated into Microsoft Bing and Microsoft Edge. Bing has received a major boost from this technology. Forever lagging behind Google on accuracy, relevancy, and popularity, Bing now finds itself surging in usage due to far greater relevancy.

Category Winner 

ChatGPT wins here but expect Google to rapidly play catch up. There are already rumors that it plans to release a snowstorm of AI-based chat products this year. The search giant has enormous incentive to make up for lost ground. 

For more information, also see: Top AI Software 

ChatGPT vs. Google Bard: Plagiarism

Google Bard does not yet have any kind of plagiarism mechanism built in. ChatGPT is well ahead in this regard. Its existing classifier checks for plagiarism with some success.

However, ChatGPT parent OpenAI admits that it is not yet fully reliable. Work is being done to help it better distinguish between AI-written and human-written text. Currently, the classifier correctly identifies 26% of AI-written text and incorrectly labeled human-written text as AI-written 9% of the time. This is a big improvement from the original classifier but it has a ways to go. This feature is particularly important when it comes to the detection of automated misinformation campaigns that take advantage of AI tools.  

OpenAI makes it clear that its classifier should never be used to condemn something as plagiarized. It helps detect potential examples with some accuracy. This helps narrow the attention of people to determine if actual plagiarism has taken place. Yet it’s also relatively inaccurate with texts below 1,000 characters, is only strong in English, and of course, AI systems can be programmed to avoid the patterns that classifiers monitor. 

Category Winner

Regardless of these cautions and shortcomings, ChatGPT wins hands down on plagiarism checking.

For more information, also see: Top Robotics Startups

ChatGPT vs. Google Bard: Customer Service-Automation

ChatGPT is probably better than Google Bard on responding to customers using a frequently asked questions format. Queries about shipping schedules, progress, product returns, product and service availability and options, as well as technical support matters seem to be relatively well handled by ChatGPT.

Thus, ChatGPT will prove valuable in lowering customer support costs, and easing the load of human representatives. It has also proven to do a decent job in analyzing information and online behavior and making product recommendations. Automation features extend to appointment scheduling, reservations, payment processing, and more.

Google Bard appears to be not so well suited to these tasks. It is more suited to time management, appointment reminders, and ensuring all steps of a process are carried out in sequence. It can be used to automate tasks like restaurant reservations and travel arrangements.

Category Winner

ChatGPT wins on customer service and automation.

For more information, also see: History of AI

ChatGPT vs. Google Bard: Pricing

ChatGPT has a basic version available for free but the main ChatGPT Plus version costs $20/month. Subscribers get access to ChatGPT at peak times, faster responses, and priority access to new features and improvements. In contrast, Google Bard is free. 

Category Winner

Given that Google Bard is currently free, it wins on pricing.

For more information, also see: Best Machine Learning Platforms

ChatGPT vs. Google Bard: Bottom Line

Both Google Bard and ChatGPT use a transformer-based AI architecture as part of a neural network that handles sequential data.

Google has the world wide web as its source of data, and thus has access to a broader data set. ChatGPT, on the other hand, harnesses a smaller, more fine-tuned neural network focused on text inputs.

Thus, each will display strengths and weaknesses due to the differences in their data sets. Use cases will arise where it becomes clear that ChatGPT is best for XYZ use cases whereas Bard is better for ABC use cases. But as one gains ground in a particular sector or use case, the other will likely innovate and add that feature, too.

Speaking generally, Google Bard looks good for text processing and summarization, whereas ChatGPT seems to perform better in chatbots, language translation, and answering questions. Some say that Google Bard brings with it a broader understanding of language, while ChatGPT brings a deeper understanding of language and how it is utilized in different contexts.

Over time, Google Bard may not only catch up, it may leap ahead due to its ability to include recent events in results, and its ability to pull data from a wider pool of information. Be aware, though, that this is all machine learning and is not actual human intelligence.  These systems have the potential to be a wonderful aid to human existence, but they will never fully replace individual brilliance or the need for human decision making.

For more information, also see: 

•  Cloud and AI Combined: Revolutionizing Tech 
• AI vs. ML 
• Algorithms and AI 

The post ChatGPT vs. Google Bard: Generative AI Comparison appeared first on eWEEK.

Enter modern cloud-based data warehouses and data management platforms such as Microsoft Azure Synapse and Snowflake. Both are well-respected data warehousing platforms and are well-rated by users on Gartner Peer Reviews. They each provide the volume, speed, and quality demanded by business intelligence and analytics applications.

But there are as many similarities as there are differences. In many cases, the choice between using Microsoft Azure Synapse and Snowflake boils down to the specific needs of the data environment. Let’s examine them both and see who comes out ahead.

Azure Synapse vs. Snowflake: Key Features

Azure Synapse

Azure Synapse, formerly the Microsoft Azure SQL Data Warehouse, is built on a strong SQL foundation and seeks to be a unified data analytics platform for big data systems and data warehouses.

Azure Synapse’s massive parallel processing architecture is designed so that its rapid processing is not wholly reliant on expensive memory. It achieves this by using clustered and non-clustered column store indexes and segments that make it easier to determine where data is stored and how it is distributed.

Snowflake

Snowflake is a relational database management system and analytics data warehouse for structured and semi-structured data. Offered via a software-as-a-service (SaaS) model, it also uses an SQL database engine to manage how information is stored in the database and process queries against virtual warehouses within the overall warehouse, each one in its own cluster node independent of others and not sharing compute resources.

Sitting on top of that are cloud services for authentication, infrastructure management, queries, and access controls. The Snowflake Elastic Data Warehouse enables users to analyze and store data utilizing Amazon S3 or Azure resources.

Which Is Best for Its Features?

This one is close, as both are top performers. For those wanting a top-class data warehouse for analytics, Snowflake narrowly wins overall. But Azure users happy to work with Power BI will find Azure Synapse a smart choice.

To learn more, also see: Top Business Intelligence Software 

Azure Synapse vs. Snowflake: Support and Ease of Use

Azure Synapse

Synapse’s reliance on SQL and Azure offers familiarity to the many people and developers who use those platforms around the world. For them, it is easy to use. In many cases, little or no training will be required.

However, those familiar with the SQL databases sometimes complain that some SQL syntax features are not available and that there are no deduplication features on table storage as well as there being no conversion tools for code.

Snowflake

The Snowflake platform is said to be user-friendly with an intuitive SQL interface that makes it easy to get it set up and running. And with 24/7 live support, Snowflake users can get assistance with any issues they may face.Snowflake automates data vacuuming, compression, diagnosis, and other features. There is also no need to copy data during scale up operations with Snowflake. In addition, Snowflake supports structured and semi-structured data.

Some users, though, state that a lack of flexibility in areas such as resizing can lead to extra expense and long hours of maintenance. And documentation is not always as thorough as it could be. And perhaps the biggest negative is lack of out-of-the-box analytics capabilities.

Which Is Best for Support and Ease of Use?

For ease of use, Azure Synapse wins, although Snowflake isn’t far behind.

Azure Synapse vs. Snowflake: Security

Azure Synapse

Azure Synapse offers data protection, access control, authentication, network security, and threat protection to help security teams identify unusual access locations, SQL injection attacks, authentication attacks, and more. Further security features include component isolation limits.

Snowflake

Snowflake boasts always-on encryption, along with network isolation and other robust security features.

Its security features come in tiers, and each higher tier costs more. But on the plus side, you don’t end up paying for security features you don’t need or want.

Which Is Best for Security?

Due to security being fully packaged within Synapse at no extra cost, Azure Synapse wins. While Snowflake offers a variety of security features useful for businesses looking to protect their data and data warehouses, many of its features are locked behind more expensive pricing tiers, which can be cost prohibitive compared to Synapse’s built-in security features.

For more information, also see: Data Mining Techniques 

Azure Synapse vs. Snowflake: Integration

Azure Synapse

Microsoft has taken its traditional Azure SQL Data Warehouse and baked in integration components such as Data Factory for efforts to outcomes (ETO) and extract, load, transform (ELT) data movement as well as Power BI for analytics. Synapse even features Spark components such as Azure Spark Pools in order to run notebooks.

Moreover, Synapse works seamlessly with all of the other Azure tools. Its Purview data cataloging system, for example, is used for data governance. This makes it easy to transform, curate, and cleanse data before it is distributed to other users for analytics. Purview also makes it relatively simple to track data lineage, refer to schema of tables, and track data movement through the system.

Snowflake

Snowflake is on the AWS Marketplace but is not tightly integrated. In some cases, users comment that it can be challenging to integrate Snowflake with other tools. But in other cases, Snowflake integrates well with applications such as Tableau, Apache Spark, IBM Cognos, and Qlik. Those using these tools will find analysis easy to accomplish.

Which Is Best for Integration?

If you live within the Azure universe, it is hard to argue with the level of integration. Power BI, for example, is right there for use in analytics with almost no work at all. Azure Synapse wins unless you are on those specific applications that Snowflake especially caters too.

Azure Synapse vs. Snowflake: Pricing

Azure Synapse

When it comes to Azure Synapse, things get a little complex. It is charged according to:

• The number of data warehouse blocks and the number of hours running.
• The amount of terabytes of data stored and processed.
• The number of instances of Apache Spark Pool running and the number of hours.
• The volume of orchestration activity runs, data movement, runtime, and cores used in data flow execution and debugging.

These are all gathered together into something called synapse commit units (SCUs), or “the amount of data that is replicated from the source database to Azure Synapse Analytics,” which can be pre-purchased according to a tiered structure. But it remains complex.

According to Azure, “The number of SCUs used is based on the amount of data that is replicated and the frequency of replication. In general, the more data you ingest and store, the more SCUs you will need and the higher the cost of using Azure Synapse Analytics will be.”

Snowflake

Snowflake keeps compute and storage separate within its pricing structure. It provides concurrency scaling automatically with all editions at no extra cost. Snowflake pricing, however, is a little complex with several editions from Basic on up, and prices rise as you move up the tiers. Roughly speaking, Snowflake is about $40 a month.

Which Is Best Based on Pricing?

The differences between Azure Synapse and Snowflake makes it difficult to do a full apples-to-apples comparison. But due to the fact that its pricing scheme is a little less complex, Snowflake wins. That said, if an analytics platform has to be purchased as part of the deployment, Azure Synapse wins, as Power BI is thrown in for free.

Since pricing varies from use case to use case, users are advised to assess the resources they expect to need to support their forecast data volume, amount of processing, and their analysis requirements. For some users, Snowflake will be cheaper; for others, Azure Synapse will come out ahead.

For more information, also see: Data Mining Techniques 

Azure Synapse and Snowflake Alternatives

Choosing Between Azure Synapse and Snowflake for Data Management

Azure Synapse and Snowflake are excellent data warehouses and data management platforms that facilitate the retention and analysis of data.

In some cases, Snowflake’s ability to split compute and storage pricing makes it more malleable to different use cases that lie outside of the open-source and developer field. Developers, too, may prefer it, depending on the platforms they utilize. Organizations working closely with Tableau, Apache Spark, IBM Cognos, and Qlik, for example, may prefer Snowflake due to its focus on those tools and platforms.

Azure Synapse, though, is highly suited to data analysis for those users familiar with SQL and operating within the Azure ecosystem. With Power BI thrown in for free to Azure and Microsoft 365 users, though, it can be tough to beat the terms offered by Microsoft in overall pricing and deep packaging discounts.

In summary, Azure Synapse probably wins for a less technical user base. Azure Synapse is better set up for users that just want to deploy a good data warehouse and analytics tool rapidly without being bogged down by configurations, data science minutia, or manual setup. Yet, it can’t be classified as a light tool or for beginners only. Far from it.

Snowflake wins for more sophisticated and higher-end users—and when the application mix within the organization favors its integration offerings. Snowflake garnered a slightly higher rating on Gartner Peer Reviews, too. Scalability and customization were further areas where users rated Snowflake higher than Azure Synapse. For those operating within the Azure ecosystem, that shouldn’t be an issue. However, for anyone building a large-scale data warehouse and data management platform, scalability and customization limitations have to be considered.

As usual, comparison between such tools comes down to user preference for platform, programming language, and existing investment in vendor platforms.

For more information, also see: Top Data Visualization Tools 

The post Azure Synapse vs. Snowflake: Data Warehouse and Data Management Comparison appeared first on eWEEK.

Along with traditional processes for accounting and finance, there are often manufacturing and supply chain processes to take into account. Factor in on-premises systems, the cloud, digital transformation and a myriad of applications, and the picture gets far more complex.

Hence, business process management (BPM) software has become vital in many businesses to keep track of everything. 

Jump to: 

• BPM Trends
• How to Select a BPM Solution
• Appian
• CMW Platform
• Blueprint Software Systems
• PMG
• Nintex
• Zvolv
• Oracle BPM
• Bizagi BPM

BPM Trends

Increased Adoption of Cloud-Based Platforms

As more and more companies move their operations to the cloud, there is a growing demand for cloud-based platforms that can manage and automate business processes. These platforms are often more cost-effective and easier to scale than on-premises solutions.

Artificial Intelligence and Machine Learning

There is a growing interest in using artificial intelligence (AI) and machine learning to automate routine human tasks and make more accurate predictions and decisions. This can help companies become more efficient and improve the overall customer experience.

“With the release of GPT, AI continues to make broad impacts in business operations, and the demand to incorporate AI into process orchestrations will continue to rise,” said Malcolm Ross, senior vice president of product strategy at Appian.

Low-Code and No-Code Platforms

There is a growing need for platforms that allow nontechnical users to create and manage business processes without needing to write code. Low-code and no-code platforms have become increasingly popular, as they enable businesses to quickly create and deploy automation solutions.

“A low-code/no-code approach lets you build operational excellence without constant third-party developer involvement,” said Michael Donaghey, vice president of sales at CMW Lab. “Citizen and semi-professional developers can make changes at a faster pace at the same time, reducing a company’s workload, delivery time, and costs.”

Increased Automation of Customer Service and Support

Companies are increasingly turning to automation to handle customer service and support tasks such as answering frequently asked questions, scheduling appointments, and handling customer complaints.

“This helps to reduce the workload on human customer service representatives and can improve the overall customer experience,” said Ross.

Robotic process automation (RPA) is a mainstream technology for automating repetitive manual processes. Buyers are looking for larger automation platforms that simply incorporate RPA and allow them to easily orchestrate processes that traverse humans, AI, systems, and digital RPA workers.

“BPM for automation is evolving beyond just modeling and designing processes for automations,” said Tony Higgins, chief product officer at Blueprint Software Systems. “Solutions are available that enable automation programs to completely understand their RPA estates with robust analytics.”

Greater Focus on Process for Regulatory Compliance

As more sensitive data is shared and stored electronically, there is a growing need for secure, compliant automation solutions. This is especially true for companies that operate in regulated industries such as healthcare and finance. BPM helps with this. 

Data fabric is a rapidly evolving foundational layer that can drive intelligent process decisions and routing. Data fabric technology is designed to integrate data from multiple systems into a single and easily managed virtualized data model.

“This is important to ensure AI can be trained with a complete view of data, and business processes can easily traverse data silos,” said Ross.

End-to-end process orchestration moves a business activity or project through its entire life cycle, pulling in and processing data through bidirectional integrations. It goes beyond simple automation of repetitive tasks to tie together human activities with digital workers.

“By using a workflow that represents an entire business process from start to finish, more strategic value is created. More complex business logic is supported, and stakeholders have real-time visibility into status and KPIs (key performance indicators,” said Joe LeCompte, CEO and principal at PMG.

Also see: Top Digital Transformation Companies

How to Select a BPM Solution

Having a BPM platform that is optimized to your particular business offers significant competitive advantages.

As such, BPM platforms should include the following core elements:

• Graphical business process or rule-modeling capabilities.
• A process repository to handle modeling metadata.
• A process execution engine /rule engine.

Some systems remain on-premises, but increasingly, cloud functionality is needed due to so many processes residing in the cloud. There is also a wide difference found in the size and scope of market offerings, and platforms can be either basic or sophisticated systems.

The other key factor: how intuitive is the user interface? BPM solutions are necessarily complex, and if only highly trained individuals will be using yours, the user interface can be correspondingly complex. Yet if you expect wider user in your company, look for a more intuitive user interface.

Top BPM Solutions

eWeek evaluated the many BPM solutions available on the market. Here are our top picks, in no particular order.

Appian

Best for: Companies seeking a low code / no code solution.

The Appian Platform is used to design digital software solutions, automate tasks to drive efficiency, and optimize business process operations. Its low-code approach simplifies the visualization and building of new applications and the establishment of workflows. It provides visibility to drive continuous optimization.

Key Differentiators

• All of Appian’s design time experiences are low and no code for faster time to solution and value.
• The platform includes all components necessary to deliver end-to-end process automations and reduce IT complexity and maintenance.
• Visualization capabilities are easy for users to implement.
• Appian helps organizations build new applications and workflows.
• Automation capabilities are available across AI, individual development plans (IDP), RPA, and application programming interface (API) integration.
• Appian offers integrated process mining and analytics.

CMW Platform

Best for: companies who want unified process automation.

The CMW Platform by CMW Lab is a low-code BPM suite built for unified process automation. It can be used for digital transformation. Building a process management system with CMW Platform is said to reduce development costs and lower the IT workload.

Key Differentiators

• It provides quick deployment on-premises, in private or public cloud.
• It uses a multi-tier hybrid software-as-a-service (SaaS) architecture and graph technologies to boost reliability and flexibility.
• Users can see first results within days.
• CMW Platform integrates scattered processes into one reliable system using API integrations, drag-and-drop capabilities, and other patented features on process management.
• CMW Platform is a customizable web-based platform to make changes on the go without developers.

Blueprint Software Systems

Best for: Companies that want to want to visualize their RPA deployment.

Blueprint enables organizations to visualize and understand their RPA estate, identify where there is waste and remove it, find retirement opportunities to reduce costs, become more efficient, and optimize any complex automations by refactoring them. It simplifies the process of migrating to another RPA platform.

Key Differentiators

• Users can migrate their RPA estates automatically with 60–75% time and cost savings when compared to manual migrations.
• Blueprint Software Systems ingests current automations, providing analytics and insight via dashboards about the complexity of automations and how many applications they interact with as well as the actions, variables, and decision branches of each process.
• Blueprint Software Systems maps RPA estates into a common object model and makes processes automatically compatible with other RPA platforms.
• Analytics help organizations better understand, optimize, and reduce operating costs.

Also see: Best Data Analytics Tools 

PMG

Best for: Companies that want a suite of dev tools.

The PMG Platform is a low-code process orchestration platform offering a suite of development tools to create business solutions for efficiency and a better user experience. Its workflow engine automates tasks, manages human activities, applies business logic, and transacts bidirectional integrations, supporting both short- and long-running processes.

Key Differentiators

• PMG’s API Builder configuration tool provides a way to augment off-the-shelf applications and solutions that aren’t easily customizable.
• If solution requirements include an end-user interface, it offers drag-and-drop configuration of portal and dashboard pages.
• As a low-code platform, businesses can leverage snippets of code they already use or ensure they have the flexibility to do so without the need for specialized tech staff.
• PMG’s Workflow Anywhere gives users the ability to run a workflow from anywhere within the platform or externally by using API Builder.
• The PGM Relay Framework offers enterprises the ability to run workflows, including PowerShell, outside of their firewall, delivering workflow capabilities while still meeting more stringent internal security guidelines.

Nintex

Best for: Companies that are focused on ease of use.

The Nintex Process Platform is a low-code process platform that helps companies discover, automate, and optimize business processes to drive growth. It offers intelligent forms, advanced workflow, digital document generation, e-signatures, RPA, process discovery technology, and a process management solution.

Key Differentiators

• Nintex Process Platform provides ease-of-use coupled capabilities for managing and automating processes.
• The platform is optimized for less technical users, like the operations and process owners intimately familiar with the processes that need to be automated and managed.
• The platform is focused on process management, automation, and improvement.
• Nintex Process Platform helps users define the processes that make the most impact to the organization.
• It provides simple process maps in collaboration with others in organization.
• It manages process participants to drive continuous process improvements.

Zvolv

Best for: Companies that want to boost hyperautomation.

Zvolv is a low-code unified platform aimed at driving hyperautomation for enterprises. It helps to accelerate digital transformation by streamlining processes across the organization with the combined use of no-code and low-code application development, automation, integrations, and analytics.

Key Differentiators

• Zvolv integrates decision-making automation and orchestration of processes across systems.
• The platform helps organizations tackle last-mile intelligent automation challenges that existing enterprise resource planning (ERP), BPM, or RPA tools cannot.
• An automation bot, low-code editor can be used by developers to enhance the application with complex use case definitions.
• Dynamic dashboards, reports, and drill-down analytics are available for decision-makers.

Also see: Top Data Visualization Tools 

Oracle BPM

Best for: large enterprise organizations.

Oracle Business Process Management Suite 12c is designed to make things simple for business users via a web-­based composer that allows them to model, simulate, optimize, deploy, and execute business processes. It also provides business-­friendly mobile and web applications as well as out-­of-­the-box process and case analytics.

Key Differentiators

• Oracle BPM offers the ability to manage by exception.
• The platform allows the modeling of structured and unstructured processes.
• Oracle BPM is a unified platform that spans systems, decisions, documents, and events.
• A light­weight Business Architecture modeling tool in the Business Process Composer provides a blueprint of the enterprise and gives a common understanding of the organization.
• Oracle BPM helps to align an organization’s goals and objectives and strategies with the actual projects that are being undertaken.

Bizagi BPM

Best for: Companies that want a common language between business and IT.

Bizagi BPM enables organizations to model, design, automate, and manage every business process on a single low-code platform. It provides the process insight and control to deliver value to the business.

Key Differentiators

• Bizagi BPM automates dynamic and complex processes enterprise-wide.
• It develops a common language between business and IT departments for faster development of applications.
• Shares, reuses, and adapts process elements to rapidly respond to changes in the market.
• Users can access real-time and historical reports to monitor business process performance and identify opportunities.
• Bizagi BPM connects modern applications, databases, or legacy systems to provide a centralized view of business data.

Also see: Top Business Intelligence Software 

The post Top Business Process Management Companies appeared first on eWEEK.

These data management applications are in heavy demand as organizations seek to harness the vast troves of data at their disposal. Without these data analytics tools, analysts and data scientists would struggle with problems such as data dispersal throughout the enterprise in multiple repositories, lack of data integration, and a variety of other data management challenges.

As both Snowflake and Informatica are leading data management platforms, users sometimes must choose between them. There are arguments for and against each.

Which of these well-respected platforms is best? Both provide the volume, speed, and quality demanded by the data analytics applications they typically support. There are as many similarities as there are differences. Yet they each have different orientations. Therefore, selection often boils down to platform preference and suitability for the organization’s data strategy.

Also see: Best Data Analytics Tools 

Snowflake vs. Informatica: Key Features

The Informatica Intelligent Data Management Cloud (IDMC) helps businesses handle dispersed and fragmented data on any platform, any cloud, on multi-cloud and multi-hybrid. It is a cloud-native, AI-powered and offers over 200 data services and it processes over 17 trillion transactions per month.

Organizations can use Informatica to share, deliver, and democratize data across lines of business and other enterprises. Data catalog scans metadata to discover and understand enterprise data. Data integration accesses and integrates data at scale using serverless computing.

Informatica’s API & App Integration connects applications and automates business processes. MDM & 360 applications provide 360 views of business data. Informatica IDMC is powered by an AI and ML engine called Claire. It can be used to discover and understand all data within and outside the enterprise, access and ingest all types of data, and curate and prepare data in a self-service fashion.

Most recently, the company announced a new suite of cloud data management services for AWS which is aimed at providing broader data management to departmental users, developers, data scientists, and data engineers across all skill levels. Informatica Data Loader on AWS is embedded directly into the Amazon Redshift console to enable movement from data ingestion to insights in minutes.

Informatica Data Marketplace supports AWS Data Exchange as part of the self-service data marketplace. Informatica INFACore supports Amazon SageMaker Studio to simplify management of complex data pipelines for building and deploying ML models.

Snowflake, in contrast, is a relational database management system and analytics data warehouse for structured and semi-structured data. Offered through the Software-as-a-Service (SaaS) model, it uses an SQL database engine to manage how information is stored in the database, and process queries against virtual warehouses within the overall warehouse, with each one of its cluster nodes independent of others and not sharing compute resources.

Sitting on top of that are cloud services for authentication, infrastructure management, queries, access controls, and so on. The Snowflake Elastic Data Warehouse enables users to analyze and store data utilizing Amazon S3 or Azure resources.

Overall, Snowflake should be regarded more as a data lake or data warehouse that facilitates analytics than a full-featured analytics application. As such, it is particularly good at managing, processing, aggregating, and sharing large amounts of data across a business. Good archiving features are also present.

Late in 2022, Snowflake released some platform updates. These included performance advancements across its single elastic engine to make it faster while improving economics for users. In addition, Snowflake’s Snowgrid technology enables customers to operate at global scale with enhancements across cross-cloud collaboration, cross-cloud data governance, and cross-cloud business continuity.

Overall, there is little to choose between the two. No clear winner here.

Snowflake vs. Informatica: Support and Ease of Use

Informatica Data Loader is a high-speed, no-cost, simple tool requiring no setup for data-savvy departmental users looking for frictionless, high-volume data loading that generates insights from data in minutes. The new functionality offers customers the ability to launch Informatica Data Loader from the Amazon Redshift console in a few clicks, easily ingesting data from AWS, on-premise, legacy systems, third-party applications, and other sources. Using a guided interface, customers can load and combine data in their data warehouse for insights into their business without having to build a custom solution. It scores well on ease of use.

The Snowflake data warehouse is said by users to be user-friendly with an intuitive SQL interface that makes it easy to get it set up and running. It automates data vacuuming, compression, diagnosis, and other features. There is no need to copy data during scale up operations with Snowflake. For third-party data sharing and access to conduct analysis, Snowflake makes the entire process much easier.

Snowflake supports structured and semi-structured data. Users also report that its ability to handle many columns is strong. But some same the documentation is weak and that a lack of out-of-the box analytics holds it back. Gartner Peer Reviews give it a good score on ease of deployment and administration.

Informatica wins narrowly in this category. 

Also see: Data Analytics Trends 

Snowflake vs. Informatica: Security

Informatica prides itself in baking in security and trust as central design principles. It does this to ensure the highest level of security and a consistent level of data quality, end-to-end data governance and data privacy across the enterprise. For enterprise users, it reduces regulatory risk by ensuring the accuracy and protection of sensitive data.

Snowflake boasts always-on encryption, along with network isolation, secure access-based requests, and other robust security features. Its security features are tiered with each higher tier costing more. That means you don’t end up paying for security features you don’t need or want.

No clear winner in this category.

Snowflake vs Informatica: Integration

Informatica is one of the few vendors that packages customer first-party data sets and third-party data sets from AWS Data Exchange to be leveraged via Informatica’s Data Marketplace. This assists in discovery, packaging, and delivery of third-party data from AWS Data Exchange. It further enables enterprise data consumers to use internal and third-party data hosted on AWS Data Exchange, which has more than 3,500 data products and more than 300 data providers. This helps to meet users via a self-service model. It can be run in multi-cloud, multi-hybrid, and on-premises infrastructures.

Snowflake is on the AWS Marketplace, which helps integrate it within that ecosystem. Some users say that with certain analytics applications, it can be challenging to integrate Snowflake. But in other analytics use cases, Snowflake is wonderfully integrated. Tableau, Apache Spark, IBM Cognos, and Qlik are all fully integrated. Those using these tools will find analysis easy to accomplish. Regardless, Gartner Peer Reviews rates Snowflake highly for integration and deployment.

Integration: Informatica narrowly wins.

Snowflake vs. Informatica: Pricing

Snowflake costs about $40 a month. But rate of usage will vary tremendously depending on the workload. Some users say large data sets cost more on Snowflake due to the way it offers separate pricing for compute and storage.

On-demand pricing is a feature of Snowflake. It also provides concurrency scaling automatically with all editions at no extra cost. Pricing, though, can be complex with four different editions from basic up, and prices rise as you move up the tiers. You can either pay for capacity upfront or choose a pay as you go model for storage.

The Informatica Processing Unit (IPU) pricing system is built around buying only the capacity you need for different services such as data integration, mass ingestion, data quality, API and App integration, and Catalog and Governance. But rates are hard to find.

Thus, differences between them make it difficult to do a full comparison. Users are advised to assess the resources they expect to need to support their forecast data volume, amount of processing, and their analysis requirements.

This is a close one as it varies from use case to use case, but Snowflake wins by a hair on pricing.

Also see: Data Mining Techniques 

Snowflake vs. Informatica: Conclusion

Snowflake and Informatica are excellent data management tools for analysis purposes. Each has its pros and cons. It all comes down to usage patterns, data volumes, workloads, and data strategies.

Both are good choices when data management, integration, and sharing are the biggest needs. Those wanting to centralize data across multiple data repositories and with large amounts of data will find both invaluable. Top-notch analytics can be added on via other platforms.

Some say Snowflake is better when you are starting small and gradually scaling up.

But these are generalities and won’t always pan out. Each business needs to research how costs will work out for them. The latest Gartner Magic Quadrant (MQ) for Data Integration Tools has Informatica scoring the highest among all vendors. Gartner did not consider Snowflake as part of that MQ. That tends to indicate that where data integration needs are highest, Informatica is the obvious choice. But for broader cloud data management needs, Snowflake may be a better choice.

Also see: What is Data Visualization

The post Snowflake vs. Informatica: Data Management Comparison appeared first on eWEEK.